Announcing SyntaxNet: The World’s Most Accurate Natural Language Parser

Re:Announcing SyntaxNet: The World’s Most Accurate Natural L... by yolesaber on Thursday May 12, 2016 @05:34PM (#11686937)

Yes, I've used that before. I'm currently using Textacy for python which is also really good. However, extracting the named entities from a sentence is still a ways off from determining what's the subject of the sentence, although it gives a good indication. Using NER + quality POS tagging and tree building should do the trick for me I think.

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Re:Announcing SyntaxNet: The World’s Most Accurate Natural L... by fatimafouda on Thursday May 12, 2016 @05:51PM (#11687046)

Are you using BOW for sentiment analysis? Also, have you tried tinkering with Watson's sentiment analyzer?

I'm working on a project that analyzes sentiment from speech, and I've been meaning to start on text sentiment analysis, but I'm not sure where to start.

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Re:Announcing SyntaxNet: The World’s Most Accurate Natural L... by yolesaber on Thursday May 12, 2016 @10:34PM (#11688257)

I'm using VADER - https://github.com/cjhutto/vaderSentiment - because it's trained on NYT data which makes it suitable for news sentiment parsing.

The code is pretty readable but relies heavily on a ruleset which might need to be tweaked for one's need.

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